BayBE — A Bayesian Back End for Design of Experiments (Merck)

A new interesting open source tool from Merck,

The Bayesian Back End (BayBE) provides a general-purpose toolbox for Bayesian Design of Experiments, focusing on additions that enable real-world experimental campaigns. Besides functionality to perform a typical recommend-measure loop, BayBE’s highlights are:

  • Custom parameter encodings: Improve your campaign with domain knowledge
  • Built-in chemical encodings: Improve your campaign with chemical knowledge
  • Single and multiple targets with min, max and match objectives
  • Custom surrogate models: For specialized problems or active learning
  • Hybrid (mixed continuous and discrete) spaces
  • Transfer learning: Mix data from multiple campaigns and accelerate optimization
  • Comprehensive backtest, simulation and imputation utilities: Benchmark and find your best settings
  • Fully typed and hypothesis-tested: Robust code base
  • All objects are fully de-/serializable: Useful for storing results in databases or use in wrappers like APIs

Link to the repo